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Lossless Image Compression using Adaptive Predictive Coding of Selected Seed Values
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Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
Recursive Prediction for Lossless Image Compression
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     This paper introduced an algorithm for lossless image compression to compress natural and medical images. It is based on utilizing various casual fixed predictors of one or two dimension to get rid of the correlation or spatial redundancy embedded between image pixel values then a recursive polynomial model of a linear base is used.

The experimental results of the proposed compression method are promising in terms of preserving the details and the quality of the reconstructed images as well improving the compression ratio as compared with the extracted results of a traditional linear predicting coding system.

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Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
Lossless and Lossy Polynomial Image Compression
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Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
Lossless and Lossy Polynomial Image Compression
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Publication Date
Wed Jul 06 2022
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Image Compression using Polynomial Coding Techniques: A review
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Publication Date
Wed Jun 01 2022
Journal Name
V. International Scientific Congress Of Pure, Applied And Technological Sciences
Lightweight Image Compression Using Polynomial and Transform Coding
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Publication Date
Wed Feb 01 2023
Journal Name
International Journal Of Engineering, Ije Transactions B: Applications
Adaptive Polynomial Coding of Multi-Base Hybrid Compression
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Publication Date
Sun Feb 24 2019
Journal Name
Iraqi Journal Of Physics
Adaptive inter frame compression using image segmented technique
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The computer vision branch of the artificial intelligence field is concerned with developing algorithms for analyzing video image content. Extracting edge information, which is the essential process in most pictorial pattern recognition problems. A new method of edge detection technique has been introduces in this research, for detecting boundaries.

           Selection of typical lossy techniques for encoding edge video images are also discussed in this research. The concentration is devoted to discuss the Block-Truncation coding technique and Discrete Cosine Transform (DCT) coding technique. In order to reduce the volume of pictorial data which one may need to store or transmit,

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Publication Date
Wed Jul 25 2018
Journal Name
International Journal Of Engineering Trends And Technology
Fixed Predictor Polynomial Coding for Image Compression
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Publication Date
Sat Jan 30 2021
Journal Name
Iraqi Journal Of Science
Image Compression Based on Arithmetic Coding Algorithm
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The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio

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Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Physics: Conference Series
Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm
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Abstract<p>Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and </p> ... Show More
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